built in R 4.0.2

Executive Summary

Domain expert want to understand major trends and insights about NYC crime/violence from several public data source to enable the police officers on patrol. This report analyzes and looks for trends that “beat officers” can use in their daily operations.

#load libraries

library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages --------------------------------------- tidyverse 1.3.0 --
v ggplot2 3.3.2     v purrr   0.3.4
v tibble  3.0.3     v dplyr   1.0.0
v tidyr   1.1.0     v stringr 1.4.0
v readr   1.3.1     v forcats 0.5.0
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(leaflet)
package 㤼㸱leaflet㤼㸲 was built under R version 4.0.3Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
#adjust options 
options(scipen = 666) # remove scientific numbers
# Read Data from internet

# df_arrests_hist <-read_csv("https://data.cityofnewyork.us/api/views/8h9b-rp9u/rows.csv?accessType=DOWNLOAD")%>%
#   janitor::clean_names()

df_arrests_hist <-read_csv("df_arrests_hist.csv")%>%
  janitor::clean_names()
Parsed with column specification:
cols(
  arrest_key = col_double(),
  arrest_date = col_character(),
  pd_cd = col_double(),
  pd_desc = col_character(),
  ky_cd = col_double(),
  ofns_desc = col_character(),
  law_code = col_character(),
  law_cat_cd = col_character(),
  arrest_boro = col_character(),
  arrest_precinct = col_double(),
  jurisdiction_code = col_double(),
  age_group = col_character(),
  perp_sex = col_character(),
  perp_race = col_character(),
  x_coord_cd = col_double(),
  y_coord_cd = col_double(),
  latitude = col_double(),
  longitude = col_double(),
  lon_lat = col_character()
)
df_arrests_ytd <- read_csv("https://data.cityofnewyork.us/api/views/uip8-fykc/rows.csv?accessType=DOWNLOAD")%>%
  janitor::clean_names()
Parsed with column specification:
cols(
  ARREST_KEY = col_double(),
  ARREST_DATE = col_character(),
  PD_CD = col_double(),
  PD_DESC = col_character(),
  KY_CD = col_double(),
  OFNS_DESC = col_character(),
  LAW_CODE = col_character(),
  LAW_CAT_CD = col_character(),
  ARREST_BORO = col_character(),
  ARREST_PRECINCT = col_double(),
  JURISDICTION_CODE = col_double(),
  AGE_GROUP = col_character(),
  PERP_SEX = col_character(),
  PERP_RACE = col_character(),
  X_COORD_CD = col_double(),
  Y_COORD_CD = col_double(),
  Latitude = col_double(),
  Longitude = col_double(),
  `New Georeferenced Column` = col_character()
)
df_shooting <- read_csv("https://data.cityofnewyork.us/api/views/833y-fsy8/rows.csv?accessType=DOWNLOAD") %>% 
  janitor::clean_names()
Parsed with column specification:
cols(
  INCIDENT_KEY = col_double(),
  OCCUR_DATE = col_character(),
  OCCUR_TIME = col_time(format = ""),
  BORO = col_character(),
  PRECINCT = col_double(),
  JURISDICTION_CODE = col_double(),
  LOCATION_DESC = col_character(),
  STATISTICAL_MURDER_FLAG = col_logical(),
  PERP_AGE_GROUP = col_character(),
  PERP_SEX = col_character(),
  PERP_RACE = col_character(),
  VIC_AGE_GROUP = col_character(),
  VIC_SEX = col_character(),
  VIC_RACE = col_character(),
  X_COORD_CD = col_number(),
  Y_COORD_CD = col_number(),
  Latitude = col_double(),
  Longitude = col_double(),
  Lon_Lat = col_character()
)

Lets begin exporing the data to see any initial insights. Lets start by taking a glimpse at the data.


glimpse(df_arrests_hist)
Rows: 5,012,956
Columns: 19
$ arrest_key        <dbl> 144026181, 144507595, 144565062, 144500188, 144216044, 144925030,...
$ arrest_date       <chr> "06/26/2015", "07/14/2015", "07/16/2015", "07/14/2015", "07/03/20...
$ pd_cd             <dbl> 639, 969, 101, 879, 478, 339, 849, 203, 511, 511, 750, 339, 847, ...
$ pd_desc           <chr> "AGGRAVATED HARASSMENT 2", "TRAFFIC,UNCLASSIFIED INFRACTION", "AS...
$ ky_cd             <dbl> 361, 881, 344, 675, 343, 341, 677, 352, 235, 235, 359, 341, 125, ...
$ ofns_desc         <chr> "OFF. AGNST PUB ORD SENSBLTY & RGHTS TO PRIV", "OTHER TRAFFIC INF...
$ law_code          <chr> "PL 2403002", "VTL051101A", "PL 1200001", "AC 010125B", "PL 16515...
$ law_cat_cd        <chr> "M", "M", "M", "V", "M", "M", "V", "M", "M", "M", "M", "M", "F", ...
$ arrest_boro       <chr> "Q", "M", "K", "Q", "M", "B", "K", "B", "B", "Q", "Q", "M", "M", ...
$ arrest_precinct   <dbl> 102, 10, 90, 103, 10, 45, 78, 47, 52, 115, 111, 30, 5, 110, 48, 1...
$ jurisdiction_code <dbl> 0, 3, 0, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0,...
$ age_group         <chr> "45-64", "25-44", "18-24", "25-44", "18-24", "18-24", "18-24", "2...
$ perp_sex          <chr> "M", "M", "F", "M", "M", "M", "M", "M", "F", "M", "M", "F", "M", ...
$ perp_race         <chr> "WHITE HISPANIC", "WHITE HISPANIC", "WHITE HISPANIC", "WHITE HISP...
$ x_coord_cd        <dbl> 1031076, 984791, 994026, 1037132, 984602, 1030990, 991330, 102801...
$ y_coord_cd        <dbl> 193779, 209846, 195548, 196129, 210686, 255310, 187303, 262766, 2...
$ latitude          <dbl> 40.69844, 40.74266, 40.70341, 40.70486, 40.74497, 40.86733, 40.68...
$ longitude         <dbl> -73.83113, -73.99805, -73.96474, -73.80927, -73.99873, -73.83101,...
$ lon_lat           <chr> "POINT (-73.83112953899997 40.69843969400005)", "POINT (-73.99804...
glimpse(df_arrests_ytd)
Rows: 103,376
Columns: 19
$ arrest_key               <dbl> 208368444, 209487362, 208853853, 209621232, 208350647, 207...
$ arrest_date              <chr> "01/22/2020", "02/13/2020", "02/01/2020", "02/16/2020", "0...
$ pd_cd                    <dbl> 729, 101, 779, 905, 259, 339, 101, 168, 779, 105, 969, 439...
$ pd_desc                  <chr> "FORGERY,ETC.,UNCLASSIFIED-FELO", "ASSAULT 3", "PUBLIC ADM...
$ ky_cd                    <dbl> 113, 344, 126, 347, 351, 341, 344, 116, 126, 106, 881, 109...
$ ofns_desc                <chr> "FORGERY", "ASSAULT 3 & RELATED OFFENSES", "MISCELLANEOUS ...
$ law_code                 <chr> "PL 1702500", "PL 1200001", "PL 215510B", "VTL11920U2", "P...
$ law_cat_cd               <chr> "F", "M", "F", "M", "M", "M", "M", "F", "F", "F", "M", "F"...
$ arrest_boro              <chr> "K", "B", "Q", "B", "B", "K", "K", "K", "Q", "Q", "K", "Q"...
$ arrest_precinct          <dbl> 69, 46, 115, 45, 43, 63, 90, 77, 103, 106, 60, 100, 46, 40...
$ jurisdiction_code        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0...
$ age_group                <chr> "25-44", "45-64", "65+", "45-64", "18-24", "18-24", "45-64...
$ perp_sex                 <chr> "M", "M", "M", "M", "F", "M", "M", "M", "F", "F", "M", "M"...
$ perp_race                <chr> "BLACK", "BLACK", "WHITE HISPANIC", "WHITE HISPANIC", "BLA...
$ x_coord_cd               <dbl> 1012161, 1011751, 1015995, 1032817, 1020183, 1003772, 1003...
$ y_coord_cd               <dbl> 178176, 250275, 212787, 257638, 239283, 161309, 197201, 18...
$ latitude                 <dbl> 40.65569, 40.85359, 40.75068, 40.87371, 40.82339, 40.60942...
$ longitude                <dbl> -73.89941, -73.90059, -73.88543, -73.82439, -73.87017, -73...
$ new_georeferenced_column <chr> "POINT (-73.89940857299997 40.655692874000074)", "POINT (-...
glimpse(df_shooting)
Rows: 21,626
Columns: 19
$ incident_key            <dbl> 74146165, 66928846, 29114164, 85180336, 73405770, 33397043,...
$ occur_date              <chr> "08/14/2010", "10/17/2009", "05/18/2007", "06/09/2012", "06...
$ occur_time              <time> 03:11:00, 18:03:00, 23:00:00, 17:15:00, 04:14:00, 23:05:00...
$ boro                    <chr> "QUEENS", "BROOKLYN", "BROOKLYN", "BROOKLYN", "BRONX", "QUE...
$ precinct                <dbl> 113, 67, 75, 81, 47, 110, 114, 113, 113, 43, 40, 110, 40, 6...
$ jurisdiction_code       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ location_desc           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
$ statistical_murder_flag <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS...
$ perp_age_group          <chr> NA, NA, NA, NA, NA, NA, "25-44", NA, NA, NA, NA, NA, NA, NA...
$ perp_sex                <chr> NA, NA, NA, NA, NA, NA, "M", NA, NA, NA, NA, NA, NA, NA, "M...
$ perp_race               <chr> NA, NA, NA, NA, NA, NA, "BLACK", NA, NA, NA, NA, NA, NA, NA...
$ vic_age_group           <chr> "25-44", "45-64", "25-44", "25-44", "25-44", "18-24", "25-4...
$ vic_sex                 <chr> "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M",...
$ vic_race                <chr> "BLACK", "BLACK", "BLACK", "BLACK", "BLACK", "BLACK", "BLAC...
$ x_coord_cd              <dbl> 1046573, 1003313, 1016292, 1005597, 1023551, 1015948, 10039...
$ y_coord_cd              <dbl> 183057, 176413, 176228, 188673, 263366, 210428, 214344, 182...
$ latitude                <dbl> 40.66891, 40.65088, 40.65033, 40.68452, 40.88947, 40.74420,...
$ longitude               <dbl> -73.77534, -73.93130, -73.88453, -73.92303, -73.85786, -73....
$ lon_lat                 <chr> "POINT (-73.77534099699994 40.66891477200004)", "POINT (-73...

summary(df_arrests_hist)
   arrest_key        arrest_date            pd_cd         pd_desc              ky_cd      
 Min.   :  9926901   Length:5012956     Min.   :  0.0   Length:5012956     Min.   :101.0  
 1st Qu.: 59318539   Class :character   1st Qu.:293.0   Class :character   1st Qu.:126.0  
 Median : 83458938   Mode  :character   Median :511.0   Mode  :character   Median :341.0  
 Mean   : 95791494                      Mean   :511.7                      Mean   :301.6  
 3rd Qu.:143546667                      3rd Qu.:750.0                      3rd Qu.:348.0  
 Max.   :206893600                      Max.   :997.0                      Max.   :995.0  
                                        NA's   :261                        NA's   :9029   
  ofns_desc           law_code          law_cat_cd        arrest_boro        arrest_precinct 
 Length:5012956     Length:5012956     Length:5012956     Length:5012956     Min.   :  1.00  
 Class :character   Class :character   Class :character   Class :character   1st Qu.: 33.00  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character   Median : 60.00  
                                                                             Mean   : 60.62  
                                                                             3rd Qu.: 84.00  
                                                                             Max.   :123.00  
                                                                                             
 jurisdiction_code  age_group           perp_sex          perp_race           x_coord_cd     
 Min.   : 0.000    Length:5012956     Length:5012956     Length:5012956     Min.   : 913357  
 1st Qu.: 0.000    Class :character   Class :character   Class :character   1st Qu.: 993370  
 Median : 0.000    Mode  :character   Mode  :character   Mode  :character   Median :1004890  
 Mean   : 1.305                                                             Mean   :1005357  
 3rd Qu.: 0.000                                                             3rd Qu.:1015835  
 Max.   :97.000                                                             Max.   :1067302  
 NA's   :10                                                                 NA's   :1        
   y_coord_cd         latitude       longitude        lon_lat         
 Min.   : 121131   Min.   :40.50   Min.   :-74.25   Length:5012956    
 1st Qu.: 186886   1st Qu.:40.68   1st Qu.:-73.97   Class :character  
 Median : 209491   Median :40.74   Median :-73.93   Mode  :character  
 Mean   : 214966   Mean   :40.76   Mean   :-73.92                     
 3rd Qu.: 236614   3rd Qu.:40.82   3rd Qu.:-73.89                     
 Max.   :8202360   Max.   :62.08   Max.   :-73.68                     
 NA's   :1         NA's   :1       NA's   :1                          
summary(df_arrests_ytd)
   arrest_key        arrest_date            pd_cd         pd_desc              ky_cd      
 Min.   :206890919   Length:103376      Min.   :  0.0   Length:103376      Min.   :101.0  
 1st Qu.:209754714   Class :character   1st Qu.:113.0   Class :character   1st Qu.:109.0  
 Median :212415780   Mode  :character   Median :339.0   Mode  :character   Median :235.0  
 Mean   :212534707                      Mean   :401.4                      Mean   :241.9  
 3rd Qu.:215249521                      3rd Qu.:639.0                      3rd Qu.:344.0  
 Max.   :218599667                      Max.   :969.0                      Max.   :995.0  
                                        NA's   :14                         NA's   :24     
  ofns_desc           law_code          law_cat_cd        arrest_boro        arrest_precinct 
 Length:103376      Length:103376      Length:103376      Length:103376      Min.   :  1.00  
 Class :character   Class :character   Class :character   Class :character   1st Qu.: 40.00  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character   Median : 62.00  
                                                                             Mean   : 63.34  
                                                                             3rd Qu.:101.00  
                                                                             Max.   :123.00  
                                                                                             
 jurisdiction_code  age_group           perp_sex          perp_race           x_coord_cd     
 Min.   : 0.000    Length:103376      Length:103376      Length:103376      Min.   : 914321  
 1st Qu.: 0.000    Class :character   Class :character   Class :character   1st Qu.: 992254  
 Median : 0.000    Mode  :character   Mode  :character   Mode  :character   Median :1005312  
 Mean   : 1.555                                                             Mean   :1005757  
 3rd Qu.: 0.000                                                             3rd Qu.:1017440  
 Max.   :97.000                                                             Max.   :1067185  
                                                                                             
   y_coord_cd        latitude       longitude      new_georeferenced_column
 Min.   :121131   Min.   :40.50   Min.   :-74.25   Length:103376           
 1st Qu.:185601   1st Qu.:40.68   1st Qu.:-73.97   Class :character        
 Median :206647   Median :40.73   Median :-73.92   Mode  :character        
 Mean   :208155   Mean   :40.74   Mean   :-73.92                           
 3rd Qu.:236150   3rd Qu.:40.81   3rd Qu.:-73.88                           
 Max.   :271820   Max.   :40.91   Max.   :-73.70                           
                                                                           
summary(df_shooting)
  incident_key        occur_date         occur_time           boro              precinct     
 Min.   :  9953245   Length:21626       Length:21626      Length:21626       Min.   :  1.00  
 1st Qu.: 51709091   Class :character   Class1:hms        Class :character   1st Qu.: 44.00  
 Median : 80495596   Mode  :character   Class2:difftime   Mode  :character   Median : 69.00  
 Mean   : 92011670                      Mode  :numeric                       Mean   : 66.23  
 3rd Qu.:140791304                                                           3rd Qu.: 81.00  
 Max.   :206891917                                                           Max.   :123.00  
                                                                                             
 jurisdiction_code location_desc      statistical_murder_flag perp_age_group    
 Min.   :0.000     Length:21626       Mode :logical           Length:21626      
 1st Qu.:0.000     Class :character   FALSE:17499             Class :character  
 Median :0.000     Mode  :character   TRUE :4127              Mode  :character  
 Mean   :0.327                                                                  
 3rd Qu.:0.000                                                                  
 Max.   :2.000                                                                  
 NA's   :2                                                                      
   perp_sex          perp_race         vic_age_group        vic_sex            vic_race        
 Length:21626       Length:21626       Length:21626       Length:21626       Length:21626      
 Class :character   Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                                               
                                                                                               
                                                                                               
                                                                                               
   x_coord_cd        y_coord_cd        latitude       longitude        lon_lat         
 Min.   : 914928   Min.   :125757   Min.   :40.51   Min.   :-74.25   Length:21626      
 1st Qu.: 999925   1st Qu.:182658   1st Qu.:40.67   1st Qu.:-73.94   Class :character  
 Median :1007645   Median :193486   Median :40.70   Median :-73.92   Mode  :character  
 Mean   :1009370   Mean   :207382   Mean   :40.74   Mean   :-73.91                     
 3rd Qu.:1016865   3rd Qu.:239187   3rd Qu.:40.82   3rd Qu.:-73.88                     
 Max.   :1066815   Max.   :271128   Max.   :40.91   Max.   :-73.70                     
                                                                                       

Let’s build a dataset that combines all the arrest data into one while removing duplicates from where they overlap.

df_arrests <- df_arrests_hist %>% 
  bind_rows(df_arrests_ytd)%>%
  distinct() %>% 
  drop_na(ofns_desc)
glimpse(df_arrests)
Rows: 5,107,279
Columns: 20
$ arrest_key               <dbl> 144026181, 144507595, 144565062, 144500188, 144216044, 144...
$ arrest_date              <chr> "06/26/2015", "07/14/2015", "07/16/2015", "07/14/2015", "0...
$ pd_cd                    <dbl> 639, 969, 101, 879, 478, 339, 849, 203, 511, 511, 750, 339...
$ pd_desc                  <chr> "AGGRAVATED HARASSMENT 2", "TRAFFIC,UNCLASSIFIED INFRACTIO...
$ ky_cd                    <dbl> 361, 881, 344, 675, 343, 341, 677, 352, 235, 235, 359, 341...
$ ofns_desc                <chr> "OFF. AGNST PUB ORD SENSBLTY & RGHTS TO PRIV", "OTHER TRAF...
$ law_code                 <chr> "PL 2403002", "VTL051101A", "PL 1200001", "AC 010125B", "P...
$ law_cat_cd               <chr> "M", "M", "M", "V", "M", "M", "V", "M", "M", "M", "M", "M"...
$ arrest_boro              <chr> "Q", "M", "K", "Q", "M", "B", "K", "B", "B", "Q", "Q", "M"...
$ arrest_precinct          <dbl> 102, 10, 90, 103, 10, 45, 78, 47, 52, 115, 111, 30, 5, 110...
$ jurisdiction_code        <dbl> 0, 3, 0, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0...
$ age_group                <chr> "45-64", "25-44", "18-24", "25-44", "18-24", "18-24", "18-...
$ perp_sex                 <chr> "M", "M", "F", "M", "M", "M", "M", "M", "F", "M", "M", "F"...
$ perp_race                <chr> "WHITE HISPANIC", "WHITE HISPANIC", "WHITE HISPANIC", "WHI...
$ x_coord_cd               <dbl> 1031076, 984791, 994026, 1037132, 984602, 1030990, 991330,...
$ y_coord_cd               <dbl> 193779, 209846, 195548, 196129, 210686, 255310, 187303, 26...
$ latitude                 <dbl> 40.69844, 40.74266, 40.70341, 40.70486, 40.74497, 40.86733...
$ longitude                <dbl> -73.83113, -73.99805, -73.96474, -73.80927, -73.99873, -73...
$ lon_lat                  <chr> "POINT (-73.83112953899997 40.69843969400005)", "POINT (-7...
$ new_georeferenced_column <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
df <- df_arrests %>% 
  rename(incident_key=arrest_key) %>%
  rename(occur_date = arrest_date) %>% 
  bind_rows(df_shooting)
glimpse(df)
Rows: 5,128,905
Columns: 29
$ incident_key             <dbl> 144026181, 144507595, 144565062, 144500188, 144216044, 144...
$ occur_date               <chr> "06/26/2015", "07/14/2015", "07/16/2015", "07/14/2015", "0...
$ pd_cd                    <dbl> 639, 969, 101, 879, 478, 339, 849, 203, 511, 511, 750, 339...
$ pd_desc                  <chr> "AGGRAVATED HARASSMENT 2", "TRAFFIC,UNCLASSIFIED INFRACTIO...
$ ky_cd                    <dbl> 361, 881, 344, 675, 343, 341, 677, 352, 235, 235, 359, 341...
$ ofns_desc                <chr> "OFF. AGNST PUB ORD SENSBLTY & RGHTS TO PRIV", "OTHER TRAF...
$ law_code                 <chr> "PL 2403002", "VTL051101A", "PL 1200001", "AC 010125B", "P...
$ law_cat_cd               <chr> "M", "M", "M", "V", "M", "M", "V", "M", "M", "M", "M", "M"...
$ arrest_boro              <chr> "Q", "M", "K", "Q", "M", "B", "K", "B", "B", "Q", "Q", "M"...
$ arrest_precinct          <dbl> 102, 10, 90, 103, 10, 45, 78, 47, 52, 115, 111, 30, 5, 110...
$ jurisdiction_code        <dbl> 0, 3, 0, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0...
$ age_group                <chr> "45-64", "25-44", "18-24", "25-44", "18-24", "18-24", "18-...
$ perp_sex                 <chr> "M", "M", "F", "M", "M", "M", "M", "M", "F", "M", "M", "F"...
$ perp_race                <chr> "WHITE HISPANIC", "WHITE HISPANIC", "WHITE HISPANIC", "WHI...
$ x_coord_cd               <dbl> 1031076, 984791, 994026, 1037132, 984602, 1030990, 991330,...
$ y_coord_cd               <dbl> 193779, 209846, 195548, 196129, 210686, 255310, 187303, 26...
$ latitude                 <dbl> 40.69844, 40.74266, 40.70341, 40.70486, 40.74497, 40.86733...
$ longitude                <dbl> -73.83113, -73.99805, -73.96474, -73.80927, -73.99873, -73...
$ lon_lat                  <chr> "POINT (-73.83112953899997 40.69843969400005)", "POINT (-7...
$ new_georeferenced_column <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ occur_time               <time> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ boro                     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ precinct                 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ location_desc            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ statistical_murder_flag  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ perp_age_group           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ vic_age_group            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ vic_sex                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ vic_race                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...

Since

I’m going to first look at historical arrest data and see what types of catagories exist. I have a feeling that some types of crimes are more frequent than others.

df%>% 
  count(ofns_desc,sort = T)
NA

There doesn’t appear to be any shooting data within this set.

Drug offenses seem to be the most frequent historical occurance within the data. I wonder how that frequency changes with time.Lets take a look at what the day averages look like each year.

It seems that arrests for dangerous drugs are on a decline.

df %>%
    filter(ofns_desc == "DANGEROUS DRUGS") %>% 
  mutate(
    occur_date = lubridate::mdy(occur_date),
         year = lubridate::year(occur_date),
         year = factor(year),
         month = lubridate:: month(occur_date),
         month = factor(month))%>%
  group_by(occur_date)%>%
  mutate(
    count_daily = n())%>%
  ungroup()%>%
  ggplot(aes(factor(year), count_daily,
    fill = year, color = year
  )) +
  geom_boxplot(alpha = 0.2, size = 1, show.legend = FALSE) +
  labs(x = NULL, y = "Daily Drug Arrests")

Prehaps in era of shrinking police budgets it would be useful to target police officer scheduling to have more police scheduled to be in generally correct locations at the right times to discourage nefarious activities and increase public safety. While Daily Drug arrests might be interesting they may not be a large danger to society. However, violent crime may be. First, lets label data to find violent crime. For this we will need to clean some of the data. Lets look at the second most common crime assualt.In this exploratory data section I’ll use this as a proxy for overall violent crimes to determine a good timeframe to use for predictions.


# The palette with grey:
df %>%
    filter(ofns_desc == "ASSAULT 3 & RELATED OFFENSES") %>% 
  mutate(
    occur_date = lubridate::mdy(occur_date),
         year = lubridate::year(occur_date),
         year = factor(year),
         month = lubridate:: month(occur_date),
         month = factor(month))%>%
  group_by(occur_date)%>%
  mutate(
    count_daily = n())%>%
  ungroup()%>%
  ggplot(aes(year, count_daily,
    fill = year, color = year, group =year)) + 
  geom_boxplot(alpha = 0.2, size = 1, show.legend = FALSE)+
  labs(x = NULL, y = "Daily Assault Arrests")

83 crime categories is alot. So let try and work some binning to reduce this dimesionality.In this case, I plan to bin by violent crimes, theft or property crimes, and other crimes.

df1 <- df1 %>% 
  mutate(
    crime_type = case_when(
      str_detect(ofns_desc, "ASSAULT")~"violent",
      str_detect(ofns_desc, "shooting")~"violent",
      str_detect(ofns_desc, "HOMICIDE")~"violent",
      str_detect(ofns_desc, "MURDER")~"violent",
      str_detect(ofns_desc, "RAPE")~"violent",
      str_detect(ofns_desc, "FORC")~"violent",
      str_detect(ofns_desc, "SEX")~"violent",
      str_detect(ofns_desc, "KID")~"violent",
      str_detect(ofns_desc, "WEAP")~"violent",
      str_detect(ofns_desc, "DANG")~"violent",
      str_detect(ofns_desc, "HARASSMENT")~"violent",
      str_detect(ofns_desc, "THEFT")~"theft_property",
      str_detect(ofns_desc, "LARCENY")~"theft_property",
      str_detect(ofns_desc, "ARSON")~"theft_property",
      str_detect(ofns_desc, "ENTRY")~"theft_property",
      str_detect(ofns_desc, "FRAUD")~"theft_property",
      str_detect(ofns_desc, "VEHICLE")~"theft_property",
      str_detect(ofns_desc, "ROBBERY")~"theft_property",
      str_detect(ofns_desc, "BURGLAR")~"theft_property",
      str_detect(ofns_desc, "FORG")~"theft_property",
      str_detect(ofns_desc, "PROP")~"theft_property",
      str_detect(ofns_desc, "PARK")~"theft_property",
      str_detect(ofns_desc, "DRIVE")~"theft_property",
      TRUE~"other"
    ),
    crime_type=factor(crime_type)
  )

df1 %>% 
  select(crime_type) %>% 
  n_distinct()
[1] 3

3 Seems to be more manigable. Now that I have crime data, Lets look for any seasonal patterns that might yield insights.For that lets examine a scatterplot of the monthly assault rates as a proxy measure for violent crimes


df1 %>% 
  filter(crime_type == "violent") %>% 
  group_by(year_mon) %>% 
  summarize(count = n()) %>%
  ungroup() %>% 
  ggplot(aes(year_mon,count))+
    geom_point()+
    geom_line()
`summarise()` ungrouping output (override with `.groups` argument)

NA
NA

Ther appears to be a seasonal spike in violent crimes during the summer months. It does appear that there is an overall lack of crime reported in 2020. liley due to COVID. With this seasonailty in mind. I plan to add NOAA weather data to the overall data set to see how weather might affect the overall daily violent crimes.

df_weather <- read_csv("https://www.ncei.noaa.gov/orders/cdo/2353296.csv") %>% 
  janitor::clean_names() %>% 
  select(date,awnd,prcp,snow,snwd,tmax,tmin,wsf2,wt01,wt02,wt03,wt04,wt06,wt08) %>% 
  replace_na(list(wt01=0,wt02=0,wt03=0,wt04=0,wt06=0,wt08=0))
Parsed with column specification:
cols(
  .default = col_double(),
  STATION = col_character(),
  NAME = col_character(),
  DATE = col_date(format = ""),
  PGTM = col_logical(),
  TAVG = col_logical(),
  TSUN = col_logical(),
  WT03 = col_logical()
)
See spec(...) for full column specifications.
1 parsing failure.
 row  col           expected actual                                               file
1096 PGTM 1/0/T/F/TRUE/FALSE   1509 'https://www.ncei.noaa.gov/orders/cdo/2353296.csv'
df_holiday <- read_csv("usholidays.csv") %>% 
  janitor::clean_names()  %>% 
  mutate(date = lubridate::mdy(date))
Parsed with column specification:
cols(
  Date = col_character(),
  Holiday = col_character()
)
mean(df_weather$awnd,na.rm = T)
[1] 5.05651

Now lets add these features to our data set.

df1 <-df1 %>% 
  inner_join(df_weather,by = c("occur_date"="date")) %>% 
  left_join(df_holiday, by = c("occur_date" = "date")) %>% 
  mutate(holiday = ifelse(is.na(holiday),0,1))
glimpse(df1)
Rows: 1,507,038
Columns: 40
$ incident_key            <dbl> 144026181, 144507595, 144565062, 144500188, 144216044, 1449...
$ occur_date              <date> 2015-06-26, 2015-07-14, 2015-07-16, 2015-07-14, 2015-07-03...
$ pd_cd                   <dbl> 639, 969, 101, 879, 478, 339, 849, 203, 511, 511, 750, 339,...
$ pd_desc                 <chr> "AGGRAVATED HARASSMENT 2", "TRAFFIC,UNCLASSIFIED INFRACTION...
$ ky_cd                   <dbl> 361, 881, 344, 675, 343, 341, 677, 352, 235, 235, 359, 341,...
$ ofns_desc               <chr> "OFF. AGNST PUB ORD SENSBLTY & RGHTS TO PRIV", "OTHER TRAFF...
$ law_code                <chr> "PL 2403002", "VTL051101A", "PL 1200001", "AC 010125B", "PL...
$ law_cat_cd              <chr> "M", "M", "M", "V", "M", "M", "V", "M", "M", "M", "M", "M",...
$ arrest_precinct         <dbl> 102, 10, 90, 103, 10, 45, 78, 47, 52, 115, 111, 30, 5, 110,...
$ jurisdiction_code       <dbl> 0, 3, 0, 0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,...
$ age_group               <chr> "45-64", "25-44", "18-24", "25-44", "18-24", "18-24", "18-2...
$ perp_sex                <chr> "M", "M", "F", "M", "M", "M", "M", "M", "F", "M", "M", "F",...
$ perp_race               <chr> "WHITE HISPANIC", "WHITE HISPANIC", "WHITE HISPANIC", "WHIT...
$ x_coord_cd              <dbl> 1031076, 984791, 994026, 1037132, 984602, 1030990, 991330, ...
$ y_coord_cd              <dbl> 193779, 209846, 195548, 196129, 210686, 255310, 187303, 262...
$ latitude                <dbl> 40.69844, 40.74266, 40.70341, 40.70486, 40.74497, 40.86733,...
$ longitude               <dbl> -73.83113, -73.99805, -73.96474, -73.80927, -73.99873, -73....
$ lon_lat                 <chr> "POINT (-73.83112953899997 40.69843969400005)", "POINT (-73...
$ occur_time              <dbl> 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 12, 11, 10, 9, 8,...
$ boro                    <chr> "QUEENS", "MANHATTAN", "BROOKLYN", "QUEENS", "MANHATTAN", "...
$ precinct                <dbl> 102, 10, 90, 103, 10, 45, 78, 47, 52, 115, 111, 30, 5, 110,...
$ statistical_murder_flag <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL...
$ year                    <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,...
$ month                   <dbl> 6, 7, 7, 7, 7, 7, 6, 7, 7, 7, 6, 7, 7, 7, 8, 7, 7, 6, 7, 6,...
$ year_mon                <yearmon> Jun 2015, Jul 2015, Jul 2015, Jul 2015, Jul 2015, Jul 2...
$ crime_type              <fct> other, other, violent, other, theft_property, theft_propert...
$ awnd                    <dbl> 4.47, 2.68, 5.59, 2.68, 2.91, 4.25, 3.13, 4.47, 4.47, 4.47,...
$ prcp                    <dbl> 0.00, 0.42, 0.00, 0.42, 0.00, 1.95, 0.00, 0.12, 0.00, 0.12,...
$ snow                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ snwd                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ tmax                    <dbl> 81, 82, 80, 82, 82, 87, 83, 81, 94, 81, 71, 88, 87, 83, 87,...
$ tmin                    <dbl> 69, 73, 64, 73, 66, 76, 65, 72, 82, 72, 58, 75, 76, 72, 72,...
$ wsf2                    <dbl> 16.1, 8.1, 15.0, 8.1, 8.9, 12.1, 13.0, 13.0, 12.1, 13.0, 16...
$ wt01                    <dbl> 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,...
$ wt02                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ wt03                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ wt04                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ wt06                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ wt08                    <dbl> 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,...
$ holiday                 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...

To do a bit of exploring, lets see where murders happen in the rain.

boro_site <- "https://data.cityofnewyork.us/api/geospatial/tqmj-j8zm?method=export&format=GeoJSON" #CAA internet blocked me from automating this
# load boros
nyboros <- geojsonio::geojson_read("json/Borough Boundaries.geojson", what = "sp") 
#prepare color mapping
nyboros$boro_name <- factor(nyboros$boro_name)
factpal <- colorFactor("Set1", nyboros$boro_name)
# build murders in the rain map
leaflet() %>% 
  addPolygons(data = nyboros, weight = 2, fillColor = ~factpal(boro_name), group = "Boros") %>% 
  setView(-74,40.7,zoom=10) %>% 
  addTiles(group = "default") %>% 
  addMarkers(data = df1 %>% filter(statistical_murder_flag == TRUE & prcp>0.1),~longitude, ~latitude,popup = ~as.character(boro), label = ~as.character(ofns_desc), group = "Shooting Murders in the Rain") %>% 
  addMarkers(data = df1 %>% filter(statistical_murder_flag == TRUE & prcp==0),~longitude, ~latitude,popup = ~as.character(boro), label = ~as.character(ofns_desc), group = "Shooting Murders in the Clear") %>% 
  addLayersControl(
    baseGroups = c("default"),
    overlayGroups = c("Boros", "Shooting Murders in the Rain", "Shooting Murders in the Clear"),
    options = layersControlOptions(collapsed = FALSE))

NA

The Map above demonstrates why I think weather is a factor. If its a rainy day there a way less shooting murders. With thisin mind I think its time to model crime. I plan to focus on building a predictive model across each boro to determine how many violent crimes, theft/property crimes and other crimes the NYPD should expect on a given month and date of week. The parameters to control will be average temp, and weather type as catorgorized in the dataset.

df2 <-  df1 %>% 
  select(occur_date,boro,month,awnd,prcp,snow,tmax,tmin,wsf2,wt01,wt02,wt03,wt04,wt06,wt08,holiday,crime_type) %>% 
  mutate(
    prcp = ifelse(prcp >0.25, 1,0),
    snow = ifelse(snow >0.25, 1,0),
    awnd = ifelse(awnd>mean(awnd,na.rm = T),1,0), # proxy for windy days
    weekday = lubridate::wday(occur_date,label = F, abbr = F)
  ) %>% 
  group_by(boro,occur_date, crime_type) %>% 
  mutate(value = n()) %>% 
  distinct() %>% 
  ungroup() %>% 
  select(-occur_date) %>% 
  na.omit()

glimpse(df2)
Rows: 28,400
Columns: 18
$ boro       <chr> "QUEENS", "MANHATTAN", "BROOKLYN", "QUEENS", "MANHATTAN", "BRONX", "BROO...
$ month      <dbl> 6, 7, 7, 7, 7, 7, 6, 7, 7, 7, 6, 7, 7, 7, 8, 7, 7, 6, 7, 6, 7, 8, 6, 7, ...
$ awnd       <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, ...
$ prcp       <dbl> 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, ...
$ snow       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ tmax       <dbl> 81, 82, 80, 82, 82, 87, 83, 81, 94, 81, 71, 88, 87, 83, 87, 75, 83, 84, ...
$ tmin       <dbl> 69, 73, 64, 73, 66, 76, 65, 72, 82, 72, 58, 75, 76, 72, 72, 69, 72, 68, ...
$ wsf2       <dbl> 16.1, 8.1, 15.0, 8.1, 8.9, 12.1, 13.0, 13.0, 12.1, 13.0, 16.1, 13.0, 12....
$ wt01       <dbl> 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, ...
$ wt02       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ wt03       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ wt04       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ wt06       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
$ wt08       <dbl> 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, ...
$ holiday    <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, ...
$ crime_type <fct> other, other, violent, other, theft_property, theft_property, other, oth...
$ weekday    <dbl> 6, 3, 5, 3, 6, 5, 5, 4, 2, 4, 7, 3, 5, 2, 4, 7, 2, 4, 4, 7, 6, 7, 1, 1, ...
$ value      <int> 72, 110, 121, 71, 111, 72, 122, 57, 95, 90, 71, 124, 96, 48, 157, 52, 68...

Since we are attempting to predict 3 outputs across 5 boros there are a few methods to use when building a regression to predict are 3 target variables.

The models aren’t great overall, but its easy to tweak and refine the assumption to build a more predictive model.In this example. I’m just using a linear model to use as to build a minimum viable product that the client can use in the future. Now that I have a model I’ll design a method to take user input and make a daily prediction. With this the NYPD could better allocate their scarce resources to the correct borrows where they should excpect abnormally high crime rates on a given day of a month.

df3 <- tibble(month = 6,
                         awmd = 15,
                         prcp = 1,
                         snow = 0,
                         tmax = 45,
                         tmin = 0,
                         wsf2 = 5.6,
                         wt01 = 1,
                         wt02 = 0,
                         wt03 = 1,
                         wt04 = 1,
                         wt06 = 0,
                         wt08 = 0,
                         holiday = 1,
                         weekday = 2))
Error: unexpected ')' in:
"                         holiday = 1,
                         weekday = 2))"
---
title: "R Notebook"
output: html_notebook
---
# built in R 4.0.2
# Executive Summary

Domain expert want to understand major trends and insights about NYC crime/violence from several public data source to enable the police officers on patrol. This report analyzes and looks for trends that "beat officers" can use in their daily operations. 



```{r}
#load libraries

library(tidyverse)
library(leaflet)

#adjust options 
options(scipen = 666) # remove scientific numbers
```

```{r}
# Read Data from internet

# df_arrests_hist <-read_csv("https://data.cityofnewyork.us/api/views/8h9b-rp9u/rows.csv?accessType=DOWNLOAD")%>%
#   janitor::clean_names()

df_arrests_hist <-read_csv("df_arrests_hist.csv")%>%
  janitor::clean_names()


df_arrests_ytd <- read_csv("https://data.cityofnewyork.us/api/views/uip8-fykc/rows.csv?accessType=DOWNLOAD")%>%
  janitor::clean_names()

df_shooting <- read_csv("https://data.cityofnewyork.us/api/views/833y-fsy8/rows.csv?accessType=DOWNLOAD") %>% 
  janitor::clean_names()

```

## Lets begin exporing the data to see any initial insights. Lets start by taking a glimpse at the data.

```{r echo = T}

glimpse(df_arrests_hist)

```
```{r}
glimpse(df_arrests_ytd)

```

```{r}
glimpse(df_shooting)

```

```{r}

summary(df_arrests_hist)

```

```{r}
summary(df_arrests_ytd)

```
```{r}
summary(df_shooting)
```




# Let's build a dataset that combines all the arrest data into one while removing duplicates from where they overlap.

```{r}
df_arrests <- df_arrests_hist %>% 
  bind_rows(df_arrests_ytd)%>%
  distinct() %>% 
  drop_na(ofns_desc)
glimpse(df_arrests)
```

```{r}
df <- df_arrests %>% 
  rename(incident_key=arrest_key) %>%
  rename(occur_date = arrest_date) %>% 
  bind_rows(df_shooting)
glimpse(df)
```
# Since 

# I'm going to first look at historical arrest data and see what types of catagories exist. I have a feeling that some types of crimes are more frequent than others.
```{r}
df%>% 
  count(ofns_desc,sort = T)
  
```
# There doesn't appear to be any shooting data within this set.
# Drug offenses seem to be the most frequent historical occurance within the data. I wonder how that frequency changes with time.Lets take a look at what the day averages look like each year. 
It seems that arrests for dangerous drugs are on a decline. 
```{r}
df %>%
    filter(ofns_desc == "DANGEROUS DRUGS") %>% 
  mutate(
    occur_date = lubridate::mdy(occur_date),
         year = lubridate::year(occur_date),
         year = factor(year),
         month = lubridate:: month(occur_date),
         month = factor(month))%>%
  group_by(occur_date)%>%
  mutate(
    count_daily = n())%>%
  ungroup()%>%
  ggplot(aes(factor(year), count_daily,
    fill = year, color = year
  )) +
  geom_boxplot(alpha = 0.2, size = 1, show.legend = FALSE) +
  labs(x = NULL, y = "Daily Drug Arrests")

```
# Prehaps in era of shrinking police budgets it would be useful to target police officer scheduling to have more police scheduled to be in generally correct locations at the right times to discourage nefarious activities and increase public safety. While Daily Drug arrests might be interesting they may not be a large danger to society. However, violent crime may be. First, lets label data to find violent crime. For this we will need to clean some of the data. Lets look at the second most common crime assualt.In this exploratory data section I'll use this as a proxy for overall violent crimes to determine a good timeframe to use for predictions.

```{r}

# The palette with grey:
df %>%
    filter(ofns_desc == "ASSAULT 3 & RELATED OFFENSES") %>% 
  mutate(
    occur_date = lubridate::mdy(occur_date),
         year = lubridate::year(occur_date),
         year = factor(year),
         month = lubridate:: month(occur_date),
         month = factor(month))%>%
  group_by(occur_date)%>%
  mutate(
    count_daily = n())%>%
  ungroup()%>%
  ggplot(aes(year, count_daily,
    fill = year, color = year, group =year)) + 
  geom_boxplot(alpha = 0.2, size = 1, show.legend = FALSE)+
  labs(x = NULL, y = "Daily Assault Arrests")
```
# It appears that a large reduction in "ASSAULT 3 & RELATED OFFENSES," occurs around 2015. I'll assume this is the mordern era of policing and use only data from 2015 on. 

```{r}
df1 <- df %>%
   mutate(
     occur_date = lubridate::mdy(occur_date),
         year = lubridate::year(occur_date),
         month = lubridate:: month(occur_date),
         year_mon = zoo::as.yearmon(occur_date, "%m/%Y"))%>% 
  filter(year >=2015) %>%
  mutate(
    ofns_desc=ifelse(is.na(ofns_desc),"shooting",ofns_desc),
         statistical_murder_flag= ifelse(is.na(statistical_murder_flag), FALSE, statistical_murder_flag),
        occur_date = lubridate::as_date(occur_date),
        occur_time = ifelse(is.na(occur_time),12:00,occur_time),
        incident_key = ifelse(is.na(incident_key),arrest_key,incident_key),
        precinct = ifelse(is.na(precinct),arrest_precinct,precinct),
        boro = ifelse(is.na(boro),arrest_boro,boro),
        boro = ifelse(boro=="M","MANHATTAN",
                      ifelse(boro=="K", "BROOKLYN",
                             ifelse(boro=="S", "STATEN ISLAND",
                                    ifelse(boro=="B", "BRONX",
                                           ifelse(boro=="Q","QUEENS",boro)))))) %>%
  select(-vic_race,-vic_sex,-vic_age_group,-perp_age_group,-location_desc,
         -arrest_boro,-new_georeferenced_column)
  
  glimpse(df1)
```

#First, I'd like to see how many crime categories there are and simplify a way to bin them. My going assumption is to focus on violent crimes, thefts or damage to property, and petty crimes. We'll see what the data yields.

```{r}
df1 %>% 
  select(ofns_desc) %>% 
  n_distinct()
```
# 83 crime categories is alot. So let try and work some binning to reduce this dimesionality.In this case, I plan to bin by violent crimes, theft or property crimes, and other crimes.

```{r}
df1 <- df1 %>% 
  mutate(
    crime_type = case_when(
      str_detect(ofns_desc, "ASSAULT")~"violent",
      str_detect(ofns_desc, "shooting")~"violent",
      str_detect(ofns_desc, "HOMICIDE")~"violent",
      str_detect(ofns_desc, "MURDER")~"violent",
      str_detect(ofns_desc, "RAPE")~"violent",
      str_detect(ofns_desc, "FORC")~"violent",
      str_detect(ofns_desc, "SEX")~"violent",
      str_detect(ofns_desc, "KID")~"violent",
      str_detect(ofns_desc, "WEAP")~"violent",
      str_detect(ofns_desc, "DANG")~"violent",
      str_detect(ofns_desc, "HARASSMENT")~"violent",
      str_detect(ofns_desc, "THEFT")~"theft_property",
      str_detect(ofns_desc, "LARCENY")~"theft_property",
      str_detect(ofns_desc, "ARSON")~"theft_property",
      str_detect(ofns_desc, "ENTRY")~"theft_property",
      str_detect(ofns_desc, "FRAUD")~"theft_property",
      str_detect(ofns_desc, "VEHICLE")~"theft_property",
      str_detect(ofns_desc, "ROBBERY")~"theft_property",
      str_detect(ofns_desc, "BURGLAR")~"theft_property",
      str_detect(ofns_desc, "FORG")~"theft_property",
      str_detect(ofns_desc, "PROP")~"theft_property",
      str_detect(ofns_desc, "PARK")~"theft_property",
      str_detect(ofns_desc, "DRIVE")~"theft_property",
      TRUE~"other"
    ),
    crime_type=factor(crime_type)
  )

df1 %>% 
  select(crime_type) %>% 
  n_distinct()

```


# 3 Seems to be more manigable. Now that I have crime data, Lets look for any seasonal patterns that might yield insights.For that lets examine a scatterplot of the monthly assault rates as a proxy measure for violent crimes 

```{r}

df1 %>% 
  filter(crime_type == "violent") %>% 
  group_by(year_mon) %>% 
  summarize(count = n()) %>%
  ungroup() %>% 
  ggplot(aes(year_mon,count))+
    geom_point()+
    geom_line()
  

```

# Ther appears to be a seasonal spike in violent crimes during the summer months. It does appear that there is an overall lack of crime reported in 2020. liley due to COVID. With this seasonailty in mind. I plan to add NOAA weather data to the overall data set to see how weather might affect the overall daily violent crimes.

```{r}
df_weather <- read_csv("https://www.ncei.noaa.gov/orders/cdo/2353296.csv") %>% 
  janitor::clean_names() %>% 
  select(date,awnd,prcp,snow,snwd,tmax,tmin,wsf2,wt01,wt02,wt03,wt04,wt06,wt08) %>% 
  replace_na(list(wt01=0,wt02=0,wt03=0,wt04=0,wt06=0,wt08=0))
df_holiday <- read_csv("usholidays.csv") %>% 
  janitor::clean_names()  %>% 
  mutate(date = lubridate::mdy(date))

mean(df_weather$awnd,na.rm = T)
```


# Now lets add these features to our data set.
```{r}
df1 <-df1 %>% 
  inner_join(df_weather,by = c("occur_date"="date")) %>% 
  left_join(df_holiday, by = c("occur_date" = "date")) %>% 
  mutate(holiday = ifelse(is.na(holiday),0,1))
glimpse(df1)
```
# To do a bit of exploring, lets see where murders happen in the rain. 
```{r}
boro_site <- "https://data.cityofnewyork.us/api/geospatial/tqmj-j8zm?method=export&format=GeoJSON" #CAA internet blocked me from automating this
# load boros
nyboros <- geojsonio::geojson_read("json/Borough Boundaries.geojson", what = "sp") 
#prepare color mapping
nyboros$boro_name <- factor(nyboros$boro_name)
factpal <- colorFactor("Set1", nyboros$boro_name)
# build murders in the rain map
leaflet() %>% 
  addPolygons(data = nyboros, weight = 2, fillColor = ~factpal(boro_name), group = "Boros") %>% 
  setView(-74,40.7,zoom=10) %>% 
  addTiles(group = "default") %>% 
  addMarkers(data = df1 %>% filter(statistical_murder_flag == TRUE & prcp>0.1),~longitude, ~latitude,popup = ~as.character(boro), label = ~as.character(ofns_desc), group = "Shooting Murders in the Rain") %>% 
  addMarkers(data = df1 %>% filter(statistical_murder_flag == TRUE & prcp==0),~longitude, ~latitude,popup = ~as.character(boro), label = ~as.character(ofns_desc), group = "Shooting Murders in the Clear") %>% 
  addLayersControl(
    baseGroups = c("default"),
    overlayGroups = c("Boros", "Shooting Murders in the Rain", "Shooting Murders in the Clear"),
    options = layersControlOptions(collapsed = FALSE))

```
# The Map above demonstrates why I think weather is a factor. If its a rainy day there a way less shooting murders. With thisin mind I think its time to  model crime. I plan to focus on building a predictive model across each boro to determine how many violent crimes, theft/property crimes and other crimes the NYPD should expect on a given month and date of week. The parameters to control will be average temp, and weather type as catorgorized in the dataset. 

```{r}
df2 <-  df1 %>% 
  select(occur_date,boro,month,awnd,prcp,snow,tmax,tmin,wsf2,wt01,wt02,wt03,wt04,wt06,wt08,holiday,crime_type) %>% 
  mutate(
    prcp = ifelse(prcp >0.25, 1,0),
    snow = ifelse(snow >0.25, 1,0),
    awnd = ifelse(awnd>mean(awnd,na.rm = T),1,0), # proxy for windy days
    weekday = lubridate::wday(occur_date,label = F, abbr = F)
  ) %>% 
  group_by(boro,occur_date, crime_type) %>% 
  mutate(value = n()) %>% 
  distinct() %>% 
  ungroup() %>% 
  select(-occur_date) %>% 
  na.omit()

glimpse(df2)
```






Since we are attempting to predict 3 outputs across 5 boros there are a few methods to use when building a regression to predict are 3 target variables. 

```{r}
library(tidymodels)
lm_model <- df2 %>% 
  group_by(boro,crime_type) %>% 
  nest() %>% 
  mutate(model= map(data,  ~lm(value~.,data=.x)),
         predicted = map2(model,data,predict))
saveRDS(lm_model,"model.RDS") # save model for later use 

eval <-  lm_model %>% 
  mutate(glance = map(model,glance)) %>% 
  unnest(glance)
  
eval
```


## The models aren't great overall, but its easy to tweak and refine the assumption to build a more predictive model.In this example. I'm just using a linear model to use as to build a minimum viable product that the client can use in the future. Now that I have a model I'll design a method to take user input and make a daily prediction. With this the NYPD could better allocate their scarce resources to the correct borrows where they should excpect abnormally high crime rates on a given day of a month. 

```{r}
test <- df2 %>% 
  group_by(boro,crime_type) %>% 
  sample_n(1) %>% 
  select(-value) %>% 
  nest()

df_test <-test %>% select(boro,crime_type) %>% ungroup()
#write_csv(df_test, "pred_df.csv")
preds <- df_test %>% 
  mutate(pred = map2(lm_model$model,test$data,predict)) %>% 
  unnest(pred)
preds
```

```{r}
df3 <- df_test %>% 
  bind_cols(tibble(month = rep(6,15),
                         awmd = 0,
                         prcp = 1,
                         snow = 0,
                         tmax = 45,
                         tmin = 0,
                         wsf2 = 5.6,
                         wt01 = 1,
                         wt02 = 0,
                         wt03 = 1,
                         wt04 = 1,
                         wt06 = 0,
                         wt08 = 0,
                         holiday = 1,
                         weekday = 2)) %>% 
  group_by(boro,crime_type) %>% 
  nest()

```

